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122 changes: 101 additions & 21 deletions LICENSE.txt
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All rights reserved.
Copyright 2019, Hudson and Thames Quantitative Research

Copyright (c) 2019, Hudson and Thames Quantitative Research
Copyright Protection Notice and Licensing Agreement

This codebase is open-source* only in the sense that the code is free to use
This codebase is open-source only in the sense that the code is free to use
as-is, and the source code is publicly available, however, all other rights
are reserved under the Hudson and Thames Quantitative Research brand.

Our intention is to make some of the techniques developed publicly available
and to promote research in quantitative finance and machine learning.

1. Users may use the code as-is.
2. All modifications made, must be added to the respective repositories
and all contribution's copyright falls under the Hudson and Thames brand.
3. Rights to reproduce, distribute, or create derivative works must be
granted in writing. Requests for permissions must be directed to
[email protected]
4. Neither the name of the copyright holder nor the names of its contributors
may be used to endorse or promote products.
5. No user may use our code or any part thereof to sell, market, or distribute for any reason whatsoever,
especially to make a profit of any kind whatsoever.

IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

1. Copyright Protection Notice:

1.1 Kindly note that the code-based platform and/or any part thereof which
includes but is not limited to algorithms, coding and/or development:
- which is currently hosted on Github, is an open source platform for
purposes of training and research.

1.2 Kindly note further that the code-based platform or any part thereof is
available to the public and/or private domain for the sole purpose of
training and research by virtue of further development and coding and
that such further development and coding in its original and/or current
format (whichever is applicable), at all material or implied and tacit
times will remain and become the Intellectual Property of the Copyright
Holder, Hudson and Thames Quantitative Research.

1.3 The code-based platform allows for the use of the code whilst the
Copyright Holder herewith automatically and directly and/or indirectly
retains and owns all rights, interest and title generated and simultaneously
granted to the Copyright Holder in terms of the International Intellectual
Property Law, either by further development or coding.

1.4 The code-based platform allows for the use of the code whilst the Copyright
Holder herewith automatically and directly and/or indirectly retains and
owns all rights, interest and title generated and simultaneously granted
to the Copyright Holder in terms of the International Intellectual Property
Law, either by further development or coding.

1.5 Any further development and/or coding of any nature whatsoever that either
enhances and/or improves the original and/or current format (whichever is
applicable) will remain the sole property and ownership of the Copyright
Holder and accordingly no claim for proprietorship and/or ownership,
alternatively monetary compensation will exist either in the past, present
or future. In simpler terms:- No person, agent or AI will have any claim of
proprietorship or ownership against the Copyright Holder for any reason of
any nature whatsoever for any development and or coding that may or may not
improve and/or enhance the platform in its original and/or current format
(whichever is applicable) and accordingly herewith irrevocably and
unconditionally renounces and waives any such claims of any nature whatsoever
either in the past, present or future.

2. Terms and Conditions of use in terms of the code-based platform:

2.1 The terms and conditions of licensing agreement shall be governed and regulated
solely in terms of this document and that this agreement supersedes any other
agreement (either historically and/or currently) entered into with the Copyright
Holder.

2.2 Any application with respect to the code-based platform and/or any part thereof
which includes but is not limited to algorithms, coding and/or development i.e.
further development and/or coding are subject to the following implied, material,
agreed, contented and tacit terms and conditions:

2.2.1 The user may use the platform in its original and/or current format
(whichever is applicable) and any content derived from such usage is
permissible and free of charge or consideration of any kind;

2.2.2 It is the distinct and strict obligation of the user to add, disclose and
release to the Copyright Holder any and all improvements, enhancements
and/or modifications (whether in source or binary form) to the respective
repositories without any undue delay. The user agrees, consents and acknowledges
that he/she/it will under no circumstances of any nature whatsoever withhold
and/or cause to withhold any improvements, enhancements and/or modifications
and as such any of the aforementioned will automatically and by default become
the sole property of the Copyright Holder, Hudson and Thames Quantitative
Research Brand, whether or not such improvements, enhancements and/or
modifications have been uploaded to the repository or not;

2.2.3 The user understands, acknowledges and consents that he/she/it must at all
material and implied times obtain the written consent of the Copyright Holder
to reproduce, distribute, duplicate or create derivative works in whole or in
part thereof (whichever is applicable) of the code-based platform for any
reason whatsoever which includes but is not limited to monetary value, further
development or research;

2.2.4 It is an explicit term and condition of this agreement that any sale, reproduction,
marketing or distribution of the code-based platform is prohibited and that any
act or conduct to commit any of the aforementioned either in whole or in part
thereof constitutes an immediate and direct breach of this agreement. The user
agrees, admits and consents that he/she/it will be liable to pay to the Copyright
Holder any damages suffered by the Copyright Holder of any nature whatsoever
(proven or not proven) as a direct and/or indirect result of any such conduct
and/or act performed in whole or in part by the user;

2.2.5 The user agrees that he/she/it will under no circumstances for any reason whatsoever
use the name of the Copyright Holder nor any affiliated and/or associated names,
details and/or information of the Copyright Holder to promote, endorse or distribute
any product using code or algorithms from this platform either as a whole or in part
thereof; and

2.2.6 This platform or any part thereof is used at the sole risk of the user and accordingly
the user irrevocably and unconditionally indemnifies and holds the Copyright Holder,
its employees and contributors harmless for any direct and/or indirect damages of any
nature whatsoever which includes but is not limited to: - incidental, exemplary, or
consequential damages for the loss of use, data or profits, or business interruption,
however caused, including any theory of liability, whether in contract, strict liability
or Tort (including negligence or otherwise arising in any way out of the use of this software),
even if advised of the possibility of such damage.
55 changes: 15 additions & 40 deletions README.md
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-----------------
# Machine Learning Financial Laboratory (mlfinlab)
[![PyPi](https://img.shields.io/pypi/v/mlfinlab.svg)]((https://pypi.org/project/mlfinlab/))
[![Python](https://img.shields.io/pypi/pyversions/mlfinlab.svg)]((https://pypi.org/project/mlfinlab/))
[![Build Status](https://travis-ci.com/hudson-and-thames/mlfinlab.svg?branch=master)](https://travis-ci.com/hudson-and-thames/mlfinlab)
[![codecov](https://codecov.io/gh/hudson-and-thames/mlfinlab/branch/master/graph/badge.svg)](https://codecov.io/gh/hudson-and-thames/mlfinlab)

![pylint Score](https://mperlet.github.io/pybadge/badges/10.svg)
[![Documentation Status](https://readthedocs.org/projects/mlfinlab/badge/?version=latest)](https://mlfinlab.readthedocs.io/en/latest/?badge=latest)

[![PyPi](https://img.shields.io/pypi/v/mlfinlab.svg)]((https://pypi.org/project/mlfinlab/))
[![Downloads](https://img.shields.io/pypi/dm/mlfinlab.svg)]((https://pypi.org/project/mlfinlab/))
[![Python](https://img.shields.io/pypi/pyversions/mlfinlab.svg)]((https://pypi.org/project/mlfinlab/))

MLFinLab is an open-source* package based on the research of Dr. Marcos Lopez de Prado ([QuantResearch.org](http://www.quantresearch.org/)) in his new books

MLFinLab is a python package based on the research of Dr. Marcos Lopez de Prado ([QuantResearch.org](http://www.quantresearch.org/)) in his new books
Advances in Financial Machine Learning, Machine Learning for Asset Managers, as well as various implementations from the [Journal of Financial Data Science](https://jfds.pm-research.com/).
This implementation started out as a spring board for a research project in the [Masters in Financial Engineering programme at WorldQuant University](https://wqu.org/) and has grown into a mini research group called [Hudson and Thames Quantitative Research](https://hudsonthames.org/) (not affiliated with the university).

Expand All @@ -24,7 +25,7 @@ The following is the online documentation for the package: [read-the-docs](https
<img src="https://raw.githubusercontent.com/hudson-and-thames/mlfinlab/master/.github/logo/support.png" height="300"><br>
</div>

A special thank you to our sponsors! It is because of your contributions that we are able to continue the development of academic research for open source. If you would like to become a sponsor and help support our research, please sign up on [Patreon](https://www.patreon.com/HudsonThames).
A special thank you to our sponsors! If you would like to become a sponsor and help support our research, please sign up on [Patreon](https://www.patreon.com/HudsonThames).

### Platinum Sponsor:
* [Machine Factor Technologies](https://machinefactor.tech/)
Expand All @@ -41,46 +42,20 @@ A special thank you to our sponsors! It is because of your contributions that we
* Егор Тарасенок
* Joseph Matthew
* Justin Gerard
* Jason
* Shaun McDonogh
* Jason Young
* [Shaun McDonogh](https://www.linkedin.com/in/shaunmcdonogh/)
* [Christian Beckmann](https://www.linkedin.com/in/christian-beckmann/)
* Jeffrey Wang
* [Eugene Tartakovsky](https://www.linkedin.com/in/etartakovsky/)
* [Ming Wu](https://www.linkedin.com/in/ming-yue-wu/)
* [Richard Scheiwe](https://www.linkedin.com/in/richardscheiwe/)
* [Tianfang Wu](file:///home/jackal08/Git/mlfinlab/docs/build/html/linkedin.com/in/tianfangwu)

---

## Getting Started

Recommended versions:
* Anaconda 3
* Python 3.6

### Installation for users
The package can be installed from the PyPi index via the console:
1. Launch the terminal and run: ```pip install mlfinlab```

### Installation for developers
Clone the [package repo](https://github.com/hudson-and-thames/mlfinlab) to your local machine then follow the steps below.

#### Installation on Mac OS X and Ubuntu Linux
1. Make sure you install the latest version of the Anaconda 3 distribution. To do this you can follow the install and update instructions found on this link: https://www.anaconda.com/download/#mac
2. Launch a terminal
3. Create a New Conda Environment. From terminal: ```conda create -n <env name> python=3.6 anaconda``` accept all the requests to install.
4. Now activate the environment with ```source activate <env name>```.
5. From Terminal: go to the directory where you have saved the file, example: cd Desktop/mlfinlab/.
6. Install Python requirements, by running the command: ```pip install -r requirements.txt```

#### Installation on Windows
1. Download and install the latest version of [Anaconda 3](https://www.anaconda.com/distribution/#download-section)
2. Launch Anaconda Navigator
3. Click Environments, choose an environment name, select Python 3.6, and click Create
4. Click Home, browse to your new environment, and click Install under Jupyter Notebook
5. Launch Anaconda Prompt and activate the environment: ```conda activate <env name>```
6. From Anaconda Prompt: go to the directory where you have saved the file, example: cd Desktop/mlfinlab/.
7. Install Python requirements, by running the command: ```pip install -r requirements.txt```

### How To Run Checks Locally
On your local machine open the terminal and cd into the working dir.
1. Code style checks: ```./pylint```
2. Unit tests: ```python -m unittest discover```
3. Code coverage: ```bash coverage```
Please find all of the supporting documentation needed here: [ReadTheDocs](https://mlfinlab.readthedocs.io/en/latest/)

---

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